California Climate Change Is Caused By ThePacific Decadal Oscillation, Not By CO

Dr. Roy ClarkVentura Photonics

SummaryThe long term trends in monthly minimum temperature from 34 California weather stations have been analyzed. These trends can be explained using a variable linear urban heat island effect superimposed on a baseline trend from the Pacific Decadal Oscillation (PDO). The majority of the prevailing California weather systems originate in the N. Pacific Ocean. The average minimum monthly temperature is a measure of the surface air temperature of these weather systems. Changes in minimum surface temperature are an indicator of changes in the temperature of the tropospheric air column, not the ground surface temperature. The PDO provides a baseline minimum temperature trend that defines the California climate variation. This allows urban heat island effects and other possible anomalous temperature measurement effects to be identified and investigated. Some of the rural weather stations showed no urban heat island effects. Stations located in urban areas showed heat island effects ranging from 0.01 to over 0.04 C/yr. The analysis of minimum temperature data using the PDO as a reference baseline has been demonstrated as a powerful technique for climate trend evaluation. The analysis found no evidence for CO2 induced warming trends in the California data. This confirms prior ‘Null Hypothesis’ work that it is impossible for a 100 ppm increase in atmospheric CO2 concentration to cause any climate change.

IntroductionIn a recent (‘Null Hypothesis’) article, Dr. Clark demonstrated from first principles that it is impossible for the observed 100 ppm increase in ‘anthropogenic’ atmospheric CO2 air-ocean interface and heat the oceans to depths of up to 100m. The penetration depth of long wave infrared (LWIR) radiation into the ocean is less than 100 µm. This is the width of a typical human hair. The increase in ‘clear sky’ downward LWIR flux from a 100 ppm increase in atmospheric CO2 concentration is 1.7 Watts per square meter. This is less than the uncertainty in the estimated long term evaporation rate or latent heat flux from the ocean surface. Over the ocean, the increase in LWIR flux from CO2 is ‘buried in the noise’ of the fluctuations in ocean evaporation from wind and surface temperature variations and changes in downward LWIR flux due to fluctuations in humidity, aerosols and cloud cover. Over land, the increase of 1.7 Watts per square meter in the LWIR flux is such a small part of the total flux at the surface that it cannot cause any measurable change in surface temperature. The and heat capacity of the ground, the surface area and angles of incidence, the balance of the upward and downward LWIR flux and the direct air convection. If the ground is moist, latent heat effects also have to be included.

It is also important to distinguish clearly between the ground surface temperature and the meteorological surface air temperature (MSAT). The MSAT is the air temperature measured in an enclosure placed 1.5 to 2 m above the ground. It depends on the origin of the bulk air mass of the local weather system, surface LWIR flux heating, air convection and wind speed. There is no simple or obvious relationship between the surface temperature and the meteorological surface air temperature (MSAT). The surface temperature needed for energy transfer analysis is the ground surface temperature. However, since there is no long term record of ground surface temperature, the MSAT record has been substituted without justification or correction for the ground surface temperature. Historically the minimum and maximum daily MSATs were recorded using Six’s thermometer. This was placed in a white painted wooden enclosure with louvered sides known in the U.S. as a cotton region shelter (CRS). During the 1980’s these were replaced with more automated thermistor devices located in smaller ‘beehive’ enclosures.[2]

The minimum surface temperature usually occurs just before sunrise and at this time the ground and air temperatures are often similar. During the day, as the sun heats the ground, the surface temperature can easily exceed 50 C under full summer sun conditions. The corresponding maximum MSAT temperature is lower, in the 20 to 25 C range because of convective air mixing near the surface. Long term changes in the minimum MSAT are an indicator of temperature changes in the bulk air mass of the prevailing weather systems. The change in minimum MSAT is propagated upwards through the troposphere as a change in the lapse rate. The heat capacity of a tropospheric air column 1 m^2 x 10 km is of the order of 8 MJ/C. The thermal energy needed to heat such an air column can usually come only from changes in ocean temperatures in the region of origin of the weather system. The increase of 1.7 Watts per square meter in ‘clear sky’ LWIR flux from a 100 ppm increase in atmospheric CO2 concentration corresponds to an increase in the daily heat load of only 0.15 MJ. This is too small to have any measurable effect on the minimum MSAT.

In the California, most of the prevailing weather systems form in the NE Pacific Ocean, so any long changes in the minimum MSAT record should be associated with changes in Pacific Ocean temperatures. An initial analysis of the monthly minimum MSAT record for 7 weather stations in the Los Angeles Region was conducted by Dr. Clark. (See the main Global Warming article on this website).[3] This demonstrated a clear relationship between the Los Angeles minimum MSAT record and the Pacific Decadal Oscillation (PDO). In addition, the difference in slope between the PDO trend and the weather station data is an indicator of the local urban heat island effect on the station record. Dr. Clark has now extended this analysis to another 27 California weather stations to investigate the effect of the PDO on the statewide climate. This analysis is presented below.

Monthly Minimum MSAT Analysis for 34 California Weather Stationsdownloaded from the Western Region Climate Center web site and used ‘as is’.[4] Pierce College data was obtained from the college website.[5] Stations with a minimum record duration of 50 years were selected to be representative of the full geographical and climate extent of California. The monthly minimum MSAT data were processed to generate a 5 year rolling average of the annual temperature anomaly, by subtracting the long term annual The monthly minimum temperature records for 34 California weather stations were mean from the annual average data. The data for each station was plotted with the 5 year rolling average of the PDO over the same time period and the linear fit to the data sets was downloaded from the University of Washington website. The long term temperature trend (C.yr-1) in the weather station data was calculated by subtracting the PDO slope from the station data. As discussed below, some of the station data showed obvious anomalies and in anomalous region. The objective of this study was to evaluate the effect of the PDO on the minimum MSAT data using a simple linear fit analysis. In some cases, the station data was ‘detrended’ to remove the linear heat island slope, but no other data processing was conducted. Figure 1 shows the 5 year rolling average for the PDO from 1904 to 2009. The linear fits to selected portions of the graph are also shown. The linear trend for the full data set is small, 0.003 C/yr. However, the slope over shorter time scales can vary substantially. In the analysis presented here, the slope of the PDO data was determined over the same time line as the temperature record data. To illustrate the analysis technique, Figure 2 shows the 5 year rolling average data for Healdsburg from 1904 to 2009. The temperature record shows a slope of almost 0.03 C/yr, indicating a probable urban heat island effect. However, the distinct ‘fingerprint’ of the PDO can still be seen superimposed on the temperature data. In this example, the temperature data were ‘detrended’ to remove the heat island slope and this data is also plotted on the graph. The recent cooling of the PDO is clearly visible in the detrended data from the late 1980s onwards.

The linear slope data for the 34 stations analyzed in this work are plotted in Figure 3 sorted using increasing slope. A number after the station name indicates a data set that was divided into four groups. The first group was ‘coastal’ which included 10 coastal weather stations from Crescent City to San Diego. The second group was ‘rural’ which included 9 stations with warming trends below 0.01 C/yr. These were mainly located in rural areas. The third group was ‘urban’ which included 14 stations with warming trends above 0.01 C/yr. Most of the reprocessed data sets fell into this category. The fourth group was ‘anomalous’ where visual inspection of the station data indicated obvious discrepancies associated for example The linear slope data for the 34 stations analyzed in this work are plotted in Figure 3 sorted with changes in location, that require further investigation. In most cases, the anomaly only using increasing slope. A number after the station name indicates a data set that was using The linear slope data for the 34 stations analyzed in this work are plotted in Figure 3 sorted increasing slope. A number after the station name indicates a data set that was increasing slope. A number after the station name indicates a data set that was reprocessed over a shorter time period to avoid obvious anomalies in the dataset. This is reprocessed over a shorter time period to avoid obvious anomalies in the dataset. This is discussed in more detail below. To facilitate a discussion of the results, the station data was divided into four groups. The first group was ‘coastal’ which included 10 coastal weather stations from Crescent City to San Diego. The second group was ‘rural’ which included 9 stations with warming trends below 0.01 C/yr. These were mainly located in rural areas. The third group was ‘urban’ which included 14 stations with warming trends above 0.01 C/yr. Most of the reprocessed data sets fell into this category. The fourth group was ‘anomalous’ where visual inspection of the station data indicated obvious discrepancies associated for example with changes in location, that require further investigation. In most cases, the anomaly only impacted part of the data set and the rest of the data could be processed normally with a reduced time scale. The warming trends for the separate station groups are plotted in Figure 4. The station locations and thumbnail plots of the data are given in Figure 5 and Figure 6. Tabular summaries, including the period of record are given in the Appendix. The warming trends for the four groups will now be considered separately.

Figure 5: Locations and thumbnail data plots of the coastal and rural stations

Figure 6: Locations and thumbnail data plots of the urban and anomalous stations

Station Group 1: Coastal

The 10 coastal stations were selected to cover a range of coastal cities along the full length of the coast of California. Because of ocean influences related to the marine layer, the coastal city temperatures do not show the large temperature fluctuations characteristic of locations further inland. This also reduces urban heat island effects. These are related to increased surface heat storage and higher ground temperatures that require both urban development and solar heating. Two of the coastal city stations, Eureka and Santa Barbara had negative temperature coefficients of -0.021 and -0.012 C/yr. Both temperature records are relatively short, 59 and 49 years and both stations were moved in the 1980s. Four of the stations, Los Angeles Airport, Monterey, Oceanside and San Francisco had temperature coefficients below 0.01 C/yr, and the remaining four, Crescent City, San Diego, Santa Cruz and Santa Monica had temperature coefficients in the 0.01 to 0.03 C/yr range. These trends are consistent with the coastal locations and urban growth patterns. More detailed analysis will require consideration of station configuration changes and microclimate effects. The temperature trend data are summarized in Table 1 (below).

Station Group 2: Rural

Nine stations were identified as ‘rural’ with temperature coefficients below 0.01 C/yr. One of these stations, Pierce College was located at the west end of the San Fernando Valley in Los Angeles, but the site location and prevailing weather conditions blocked any urban heat island effect from Los Angeles. The rural sites covered a wide range of climate zones, from Fort Bidwell in the NE corner of the state with an annual average minimum temperature of 1 C to Needles, on the lower Colorado River with an annual average minimum temperature of 16 C. In general, the 5 year averages of the minimum temperatures tracked the 5 year average of the PDO. The Wasco station record contained two negative temperature peaks greater that 2 C near 1910 and 1920 that increased the overall temperature coefficient. However, when the data were processed from 1934 onwards, the 75 year data set fell in the rural category. The rural temperature trend data are summarized in Table 2 (below).

Station Group 3: Urban

Fifteen stations were identified as urban. However this group included 8 datasets that were reprocessed with shorter time scales to avoid obvious data anomalies. For two of these stations, Nevada City and Bakersfield, the data anomalies occurred in the central region of the complete dataset, so the data were reprocessed as two separate subsets. This gave a total of seventeen urban datasets with temperature coefficients between 0.01 and 0.08 C/yr. In this group, Sacramento had the smallest temperature coefficient, 0.01 C/yr and Bakersfield 2 (reprocessed) had the largest, 0.078 C/yr. The urban temperature trend data are summarized in Table 3 (below).

Station Group 4: Anomalous

Seven stations had temperature records that showed anomalous behavior. Blythe, Visalia and Wasco had obvious negative peaks near the start of the temperature records. Trona had a large positive peak near the end of the temperature record. In these cases, a shorter record was processed that did not include the anomalous region. The length of the new record was selected by simple visual inspection of the data. The record for Bakersfield showed a significant decrease between 1980 and 1990, so the record was split between 1984 and 1985 and the two sets were processed separately. The observed decrease does not appear to be associated with any station relocation. The record for Nevada City showed a significant increase between 1970 and 1980. This may be associated with a relocation of the station in 1976. The record was split and the two sections either side of the shift were processed separately. The Tejon Ranch station showed a large negative coefficient. Part of this may be attributed to land use changes and a shift from ranching to irrigated crops that began in the late 1930’s. However, recent data after 2003 showed another large negative shift of nearly 10 C, so these recent data were not included in the analysis. There are land use, site and instrument bias issues that need to be investigated for this station. The urban temperature trend data are summarized in Table 4.

ConclusionsThe dominant factor that determines the climate of the State of California is the variation in N. analysis of the long term minimum temperature data from 34 widely spaced weather stations. The PDO record provides a baseline that can be used to identify urban heat island effects and anomalous data in the weather station records. This provides a powerful technique for investigating climate change in California and may be extended to other Western States and other areas where there is a significant ocean influence on climate. The analysis presented here used a simple linear fit to the station data. By combining the weather station data with other meteorological data and climate simulations, a more detailed analysis of the effect the PDO and other factors on the climate of the State of California may be performed. However, this is not a ‘one size fits all’ approach and each data set needs to be examined carefully on a case by case basis to evaluate all of the factors that may bias the data. These results also confirm earlier work in which Dr. Clark demonstrated that it was impossible for the observed changes in atmospheric CO2 concentration to cause any climate change. There is no CO2‘signature’ in any of the temperature records that were analyzed. The recent decrease in the PDO with the triple peak ‘signature’ from 1985 onwards is clearly visible in most of the temperature data sets. Predictions for CO2 induced global warming indicate a monotonically increasing ‘equilibrium surface temperature’ for this period. The empirical concept of CO2induced global warming has no basis in the physical reality of climate change.

Find Out More - Read the Book:The Dynamic Greenhouse Effect and Climate Averaging Paradox For more information click here.

Understanding the Meteorological Surface Air Temperature Record

The publication of the Berkeley Earth Surface Temperature or BEST analysis by Prof. Richard Muller has added little, except perhaps some more confusion to our understanding of the weather station temperature record. However, rather than criticize the BEST work, the important question is what could be done differently that would lead to a better understanding of climate change?

The first point is that we are still stuck with the same data set. So we need a better understanding of what the measurements really mean. Instead of analyzing the information as an abstract mathematical data set, we need to treat the measurements as real physical variables.

The temperature used in the climate record is the meteorological surface air temperature (MSAT) measured in an enclosure placed at eye level above the ground. Historically, the daily maximum and minimum temperatures were recorded using Six’s thermometer. The maximum temperature is usually recorded in the early afternoon, after the noontime peak in the solar flux. The minimum temperature is usually recorded near sunrise.

The minimum MSAT is essentially a measure of the bulk air temperature of the local weather system as it is passing through. In combination with the local humidity, it is an approximate indicator of the local lapse rate. It is also an approximate indicator of the real ground surface temperature under the thermometer. In order to change the minimum MSAT, the whole air column up to the tropopause has to be warmed. (There are exceptions, such as local compressive downdrafts that produce strong local adiabatic heating. Santa Ana winds in S. California are a good example of this).

As discussed in detail in the article on the California climate below, the minimum MSAT is determined by the temperatures in the region of origin or approach of the prevailing weather systems. In the case of California, this is the northern Pacific Ocean in the region of the Aleutian Islands. This means that the Pacific Decadal Oscillation (PDO) should be clearly detectable in the California minimum MSAT weather station record. The PDO provides a reference temperature trend that can be used as a probe of the local weather station record.

The maximum MSAT is essentially a measure of the maximum solar heating of the surface that is coupled to the MSAT thermometer by moist convection. As the temperature difference between the surface and the MSAT thermometer increases, the volume of the airflow produced by the moist convection increases. It is usually assumed that the heat removed by convection directly proportional to the temperature difference. Under full summer sun conditions, this temperature difference can easily reach 30 K and the dry ground temperature can exceed 325 K (52 C). There is no simple or obvious relationship between the ground surface temperature and the MSAT.

However, the way to probe the surface heating effects is to examine the minimum surface temperature and the daily delta or temperature increase from minimum to maximum. To illustrate this approach, consider the daily min/max temperature record for Los Angeles from 1921 to 2008. Figure 1 shows the average daily minimum temperature for the 87 year period. The ±1 sigma points are also plotted, calculated using the Excel ‘stdev’ formula. The one sigma deviation curve is also plotted below the data. The minimum average temperature of 8.5 C occurs in early January. The maximum temperature of 18 C occurs at the end of July. The peak solar flux occurs with the summer solstice in late June. The minimum MSAT curve shows the classic behavior of a thermal storage reservoir. The peak does not occur until about 6 weeks after the peak in the solar flux. This lag or phase sift is characteristic of the ground thermal storage. In addition, the curve is not symmetric about the peak. The rise time is longer than the fall time. This is because the rainy season in Los Angeles occurs in the winter. The ground has to dry out as it is warming up and additional heat is required to evaporate the moisture. The Los Angeles climate is also bimodal. If the prevailing winds are from the ocean, the humidity is higher and there is morning cloud from the marine layer. If the prevailing winds are from the inland desert regions, the humidity is lower. There is less cloud and the winds warm by adiabatic compression as they descend about 1 km from the inland high desert to the coast. This can increase temperatures by 10 C. The desert-ocean transitions occur more frequently in winter than in summer, so the standard deviation in the temperature is higher in winter and reaches a minimum in the summer.

Figure 2 shows the average maximum MSAT for the 87 year period. This also shows the characteristic July peak after the summer solstice. The average minimum temperature is 19 C in January and 29 C in late July. There is also the same minimum in the standard deviation during the summer. In addition, there is a shoulder in the curve in early June. This is often a period of cloudiness associated with a strong marine layer known locally as ‘June gloom’. This reduces the solar heating and slows the seasonal temperature rise.

Figure 3 shows the average daily delta (max-min) temperature rise for the 87 year period. The average temperature rise is close to 10 C throughout the year. It decreases to 8 C during June, because of cloudiness and reaches a summer maximum of 11.5 C in July and August. The standard deviation also shows the characteristic minimum in the summer. The principal reason that the average temperature difference remains near 10 C is related to the nature of the convection. Convective cooling requires the direct heating of the air adjacent to the surface. The heated air then rises and is replaced by cooler air from above. The convective or sensible heat flux depends on the temperature difference between the ground and the air. The heat capacity and buoyancy of the air does not change significantly with the seasons. In the Los Angeles area, the total cumulative daily solar flux in winter is near 10 MJ.m^-2 and the summer peak is near 26 MJ.m^-2. In winter, the maximum surface temperature is approximately 10 C higher than the air temperature. In summer it is more than 20 C higher than the air temperature. This is based on Ameriflux data from the Irvine ‘Grasslands’ site. Therefore, the cooling convective air flow from the surface is also more than doubled from winter to summer. This acts to limit the daily rise in the air temperature as the solar flux increases.

Most of the urban development in the Los Angeles area occurred after 1950. Figure 4 shows the average daily minimum MSAT for the periods 1921 to 1950 and 1951 to 2008. After 1950, the MSAT peak in the average minimum temperature increased by 2 C. However, during November and December, the minimum temperatures are lower after 1950. The urban heat island retains more heat during the summer, but cools off faster as the solar flux decreases in the late fall. For clarity, Figure 5 shows the temperature difference between the two plots in Figure 4. This clearly shows the urban heat island (UHI) effect for Los Angeles.

As discussed in detail below, the minimum MSAT record for weather stations in California shows a characteristic signature from the Pacific Decadal Oscillation, PDO. This may be used as a reference temperature baseline that can be compared to the local weather station record. The difference in slopes between the two data sets over the period of record is an approximate indicator of the UHI that has occurred and may also indicate other station bias effects. This technique is not limited to California, but may be used in any area where there is a clearly identifiable ocean region of origin for the predominant local weather systems. For example, this technique was extended to the UK using the local Atlantic Multi-decadal Oscillation (MDO) as the reference. The basic technique is illustrated for Los Angeles and Heathrow in Figures 6 and 7. The results for over 30 weather stations in California and in the UK are summarized in Figures 8 and 9.

Figure 6: Minimum MSAT temperature, 5 year rolling average, for the LA Civic Center from 1925 to 2005. The PDO and the trend lines over the same time period are also shown.

Figure 7: Linear trend analysis for Heathrow using the AMO as reference, 5 year rolling average.

Figure 8: Linear warming trend data for the California weather stations. The stations were divided into four groups based on location and linear trend magnitude.

Figure 9: Linear warming trend data for the UK weather stations. The stations were divided into three groups based on linear trend magnitude.